LST-AI: A deep learning ensemble for accurate MS lesion segmentation
•Free and open-source lesion-segmentation tool (LST-AI), allowing widespread use and community contributions.•Tested on multiple external datasets and consistently outperforms existing models.•Ensemble of 3D U-Nets and composite loss functions to optimize performance.•Enhanced detection rate, especi...
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| Published in | NeuroImage clinical Vol. 42; p. 103611 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier Inc
2024
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2213-1582 2213-1582 |
| DOI | 10.1016/j.nicl.2024.103611 |
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| Abstract | •Free and open-source lesion-segmentation tool (LST-AI), allowing widespread use and community contributions.•Tested on multiple external datasets and consistently outperforms existing models.•Ensemble of 3D U-Nets and composite loss functions to optimize performance.•Enhanced detection rate, especially for small lesions 10–100 mm3.•Includes lesion location annotation per 2017 McDonald criteria.
Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D U-Nets.
LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1-weighted and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI also includes a lesion location annotation tool, labeling lesions as periventricular, infratentorial, and juxtacortical according to the 2017 McDonald criteria, and, additionally, as subcortical. We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models.
Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63—surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10 mm3 and 100 mm3. Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms. |
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| AbstractList | •Free and open-source lesion-segmentation tool (LST-AI), allowing widespread use and community contributions.•Tested on multiple external datasets and consistently outperforms existing models.•Ensemble of 3D U-Nets and composite loss functions to optimize performance.•Enhanced detection rate, especially for small lesions 10–100 mm3.•Includes lesion location annotation per 2017 McDonald criteria.
Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D U-Nets.
LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1-weighted and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI also includes a lesion location annotation tool, labeling lesions as periventricular, infratentorial, and juxtacortical according to the 2017 McDonald criteria, and, additionally, as subcortical. We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models.
Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63—surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10 mm3 and 100 mm3. Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms. Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D U-Nets. LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1-weighted and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI also includes a lesion location annotation tool, labeling lesions as periventricular, infratentorial, and juxtacortical according to the 2017 McDonald criteria, and, additionally, as subcortical. We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models. Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63-surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10 mm and 100 mm . Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms. Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D U-Nets. LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1-weighted and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI also includes a lesion location annotation tool, labeling lesions as periventricular, infratentorial, and juxtacortical according to the 2017 McDonald criteria, and, additionally, as subcortical. We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models. Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63-surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10 mm3 and 100 mm3. Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms.Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D U-Nets. LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1-weighted and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI also includes a lesion location annotation tool, labeling lesions as periventricular, infratentorial, and juxtacortical according to the 2017 McDonald criteria, and, additionally, as subcortical. We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models. Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63-surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10 mm3 and 100 mm3. Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms. Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D U-Nets.LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1-weighted and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI also includes a lesion location annotation tool, labeling lesions as periventricular, infratentorial, and juxtacortical according to the 2017 McDonald criteria, and, additionally, as subcortical. We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models.Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63—surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10 mm3 and 100 mm3. Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms. Highlights•Free and open-source lesion-segmentation tool (LST-AI), allowing widespread use and community contributions. •Tested on multiple external datasets and consistently outperforms existing models. •Ensemble of 3D U-Nets and composite loss functions to optimize performance. •Enhanced detection rate, especially for small lesions 10–100 mm 3. •Includes lesion location annotation per 2017 McDonald criteria. |
| ArticleNumber | 103611 |
| Author | Kofler, Florian Mühlau, Mark Metz, Marie Schinz, David Menze, Bjoern Wiltgen, Tun Berthele, Achim Schmitz-Koep, Benita Zimmer, Claus Sepp, Dominik Bischl, Daria Kirschke, Jan Schlaeger, Sarah Wiestler, Benedikt Voon, CuiCi McGinnis, Julian Prucker, Philipp Rueckert, Daniel Hemmer, Bernhard Will, Nikolaus Grundl, Lioba |
| Author_xml | – sequence: 1 givenname: Tun orcidid: 0000-0002-2682-3641 surname: Wiltgen fullname: Wiltgen, Tun organization: Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 2 givenname: Julian surname: McGinnis fullname: McGinnis, Julian organization: Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 3 givenname: Sarah surname: Schlaeger fullname: Schlaeger, Sarah organization: Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 4 givenname: Florian surname: Kofler fullname: Kofler, Florian organization: Department of Computer Science, Institute for AI in Medicine, Technical University of Munich, Munich, Germany – sequence: 5 givenname: CuiCi surname: Voon fullname: Voon, CuiCi organization: Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 6 givenname: Achim surname: Berthele fullname: Berthele, Achim organization: Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 7 givenname: Daria surname: Bischl fullname: Bischl, Daria organization: Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 8 givenname: Lioba surname: Grundl fullname: Grundl, Lioba organization: Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 9 givenname: Nikolaus surname: Will fullname: Will, Nikolaus organization: Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 10 givenname: Marie surname: Metz fullname: Metz, Marie organization: Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 11 givenname: David surname: Schinz fullname: Schinz, David organization: Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 12 givenname: Dominik surname: Sepp fullname: Sepp, Dominik organization: Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 13 givenname: Philipp surname: Prucker fullname: Prucker, Philipp organization: Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 14 givenname: Benita surname: Schmitz-Koep fullname: Schmitz-Koep, Benita organization: Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 15 givenname: Claus surname: Zimmer fullname: Zimmer, Claus organization: Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 16 givenname: Bjoern surname: Menze fullname: Menze, Bjoern organization: Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland – sequence: 17 givenname: Daniel surname: Rueckert fullname: Rueckert, Daniel organization: Department of Computer Science, Institute for AI in Medicine, Technical University of Munich, Munich, Germany – sequence: 18 givenname: Bernhard surname: Hemmer fullname: Hemmer, Bernhard organization: Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 19 givenname: Jan surname: Kirschke fullname: Kirschke, Jan organization: Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 20 givenname: Mark orcidid: 0000-0002-9545-2709 surname: Mühlau fullname: Mühlau, Mark email: mark.muehlau@tum.de organization: Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany – sequence: 21 givenname: Benedikt surname: Wiestler fullname: Wiestler, Benedikt organization: Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38703470$$D View this record in MEDLINE/PubMed |
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| Keywords | RRMS N/A CNN PPMS MRI PV Lesion Segmentation CPU SAMSEG GPU SC Multiple Sclerosis White Matter Lesions ASD PPVL DSC WM PPV FLAIR ON SPMS MS Artificial Intelligence AI IQR IT CIS LST LST-LPA Deep Learning TE Magnetic Resonance Imaging SensL TI LST-LGA JC ReLU FA T1w TR positive predictive value not applicable/available central processing unit inversion time artificial intelligence average surface distance echo time convolutional neural networks lesion-wise positive predictive value secondary progressive multiple sclerosis rectified linear unit lesion segmentation tool lesion growth algorithm repetition time T1-weighted optic neuritis subcortical primary progressive multiple sclerosis periventricular flip angle magnetic resonance imaging lesion-wise sensitivity interquartile range white matter lesion segmentation tool lesion prediction algorithm fluid-attenuated inversion recovery dice similarity coefficient multiple sclerosis infratentorial sequence adaptive multimodal segmentation relapsing-remitting multiple sclerosis lesion segmentation tool juxtacortical graphics processing unit clinically isolated syndrome |
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| Snippet | •Free and open-source lesion-segmentation tool (LST-AI), allowing widespread use and community contributions.•Tested on multiple external datasets and... Highlights•Free and open-source lesion-segmentation tool (LST-AI), allowing widespread use and community contributions. •Tested on multiple external datasets... Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade... |
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| SubjectTerms | Adult Artificial Intelligence Brain - diagnostic imaging Brain - pathology Deep Learning Female Humans Image Processing, Computer-Assisted - methods Lesion Segmentation Magnetic Resonance Imaging Magnetic Resonance Imaging - methods Male Multiple Sclerosis Multiple Sclerosis - diagnostic imaging Multiple Sclerosis - pathology Neuroimaging - methods Neuroimaging - standards Radiology White Matter - diagnostic imaging White Matter - pathology White Matter Lesions |
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| Title | LST-AI: A deep learning ensemble for accurate MS lesion segmentation |
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